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Python Data Science Course & Training

Python for Data Science course enables you to master Data Science Analytics using Python. You will work on various python libraries like SciPy, NumPy, Matplotlib, Lambda function, etc. You will master data science analytics skills through real-world projects in multiple domains like Big Data, Data Science and Machine Learning.

Key Features

39 Hrs Instructor Led Training
24 Hrs Self-paced Videos
50 Hrs Project work & Exercises
Certification and Job Assistance
Flexible Schedule
Lifetime Free Upgrade
24 x 7 Lifetime Support & Access

Python Data Science Overview

Python for Data Science Course helps you learn the python programming required for Data Science. This python for Data Science training you will master the technique of how Python is deployed for Data Science, work with Pandas library for Data Science, data cleaning, data visualization, Machine Learning, advanced numeric analysis,etc. along with real-world projects and case studies.

What will you learn in this Python for Data Science training?

  1. Introduction to Python for Data Science
  2. OOP concepts, expressions and functions
  3. What is SQLite in Python, operations and classes
  4. Creating Pig and Hive UDF in Python
  5. Deploying Python for MapReduce programming
  6. Real-world Python for Data Science projects
  • BI Managers and Project Managers
  • Software Developers and ETL Professionals
  • Analytics Professionals
  • Big Data Professionals
  • Those who are wanting to have a career in Python

You don’t need any specific knowledge for this Python for Data Science course. Though, a basic knowledge of programming can help.

  • Python’s design and libraries provide 10 times productivity compared to C, C++ or Java
  • A Senior Python Developer in the United States can earn $102,000 – indeed.com

Python is one of the best programming languages that is used for the domain of Data Science. Intellipaat is offering the definitive Python for Data Science course for learning Python coding, running it on various systems like Windows, Linux and Mac thus making it one of the highly versatile languages for the domain of Data Analytics. Upon the completion of this Data Science with Python training, you will be able to get the best jobs in the Data Science domain for top salaries.

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Course Fees

Self Paced Training

  • 24 Hrs e-learning videos
  • Lifetime Free Upgrade
  • 24 x 7 Lifetime Support & Access
$176

Online Classroom preferred

  • Everything in self-paced, plus
  • 39 Hrs of instructor-led training
  • 1:1 doubt resolution sessions
  • Attend as many batches for Lifetime
  • Flexible Schedule
  • 16 Aug
  • SAT - SUN
  • 08:00 PM TO 11:00 PM IST (GMT +5:30)
  • 18 Aug
  • TUE - FRI
  • 07:00 AM TO 09:00 AM IST (GMT +5:30)
  • 22 Aug
  • SAT - SUN
  • 08:00 PM TO 11:00 PM IST (GMT +5:30)
  • 30 Aug
  • SAT - SUN
  • 08:00 PM TO 11:00 PM IST (GMT +5:30)
$281 10% OFF Expires in
$0

Corporate Training

  • Customized Learning
  • Enterprise grade learning management system (LMS)
  • 24x7 support
  • Strong Reporting

Python for Data Science Course Content

Module 01 - Introduction to Data Science using Python preview videos

1.1 What is Data Science, what does a data scientist do
1.2 Various examples of Data Science in the industries
1.3 How Python is deployed for Data Science applications
1.4 Various steps in Data Science process like data wrangling, data exploration and selecting the model.
1.5 Introduction to Python programming language
1.6 Important Python features, how is Python different from other programming languages
1.7 Python installation, Anaconda Python distribution for Windows, Linux and Mac
1.8 How to run a sample Python script, Python IDE working mechanism
1.9 Running some Python basic commands
1.10 Python variables, data types and keywords.

Hands-on Exercise – Installing Python Anaconda for the Windows, Linux and Mac

2.1 Introduction to a basic construct in Python
2.2 Understanding indentation like tabs and spaces
2.3 Python built-in data types
2.4 Basic operators in Python
2.5 Loop and control statements like break, if, for, continue, else, range() and more.

Hands-on Exercise –
1.Write your first Python program
2. Write a Python function (with and without parameters)
3. Use Lambda expression
4. Write a class
5. Create a member function and a variable
6. Create an object and write a for loop to print all odd numbers

3.1 Central Tendency
3.2 Variabiltiy
3.3 Hypothesis Testing
3.4 Anova
3.5 Correlation
3.6 Regression
3.7 Probability Definitions and Notation
3.8 Joint Probabilities
3.9 The Sum Rule, Conditional Probability, and the Product Rule
3.10 Baye’s Theorem

Hands-on Exercise –
1. We will analyze both categorical data and quantitative data
2. Focusing on specific case studies to help solidify the week’s statistical concepts

4.1 Understanding the OOP paradigm like encapsulation, inheritance, polymorphism and abstraction
4.2 What are access modifiers, instances, class members
4.3 Classes and objects
4.4 Function parameter and return type functions
4.5 Lambda expressions.

Hands-on Exercise –
1. Writing a Python program and incorporating the OOP concepts

5.1 Introduction to mathematical computing in Python
5.2 What are arrays and matrices, array indexing, array math, Inspecting a numpy array, Numpy array manipulation

Hands-on Exercise –
1. How to import numpy module
2. Creating array using ND-array
3. Calculating standard deviation on array of numbers and calculating correlation between two variables.

6.1 Introduction to scipy, building on top of numpy
6.2 What are the characteristics of scipy
6.3 Various subpackages for scipy like Signal, Integrate, Fftpack, Cluster, Optimize, Stats and more, Bayes Theorem with scipy.

Hands-on Exercise:
1. Importing of scipy
2. Applying the Bayes theorem on the given dataset.

7.1 What is a data Manipulation. Using Pandas library
7.2 Numpy dependency of Pandas library
7.3 Series object in pandas
7.4 Dataframe in Pandas
7.5 Loading and handling data with Pandas
7.6 How to merge data objects
7.7 Concatenation and various types of joins on data objects, exploring dataset

Hands-on Exercise –
1. Doing data manipulation with Pandas by handling tabular datasets that includes variable types like float, integer, double and others.
2. Cleaning dataset, Manipulating dataset, Visualizing dataset

8.1 Introduction to Matplotlib
8.2 Using Matplotlib for plotting graphs and charts like Scatter, Bar, Pie, Line, Histogram and more
8.3 Matplotlib API

Hands-on Exercise –
1. Deploying Matplotlib for creating pie, scatter, line and histogram.
2. Subplots and Pandas built-in data visualization.

9.1 Revision of topics in Python (Pandas, Matplotlib, numpy, scikit-Learn)
9.2 Introduction to machine learning
9.3 Need of Machine learning
9.4 Types of machine learning and workflow of Machine Learning
9.5 Uses Cases in Machine Learning, its various arlogrithms
9.6 What is supervised learning
9.7 What is Unsupervised Learning

Hands-on Exercise –
1. Demo on ML algorithms

10.1 What is linear regression
10.2 Step by step calculation of Linear Regression
10.3 Linear regression in Python
10.4 Logistic Regression
10.5 What is classification
10.6 Decision Tree, Confusion Matrix, Random Forest, Naïve Bayes classifier (Self paced), Support Vector Machine(self paced), xgboost(self paced)

Hands-on Exercise – Using Python library Scikit-Learn for coming up with Random Forest algorithm to implement supervised learning.

11.1 Introduction to unsupervised learning
11.2 Use cases of unsupervised learning
11.3 What is clustering
11.4 Types of clustering(self-paced)-Exclusive clustering, Overlapping Clustering, Hierarchical Clustering(self-paced)
11.5 What is K-means clustering
11.6 Step by step calculation of k-means algorithm
11.7 Association Rule Mining(self-paced), Market Basket Analysis(self-paced), Measures in association rule mining(self-paced)-support, confidence, lift
11.8 Apriori Algorithm

Hands-on Exercise –
1. Setting up the Jupyter notebook environment
2. Loading of a dataset in Jupyter
3. Algorithms in Scikit-Learn package for performing Machine Learning techniques and training a model to search a grid.
4. Practice on k-means using Scikit
5. Practice on Apriori

12.1 Introduction to pyspark
12.2 Who uses pyspark, need of spark with python
12.3 Pyspark installation
12.4 Pyspark fundamentals
12.5 Advantage over mapreduce, pyspark
12.6 Use-cases pyspark  and demo.

Hands-on Exercise:
1. Demonstrating Loops and Conditional Statements
2. Tuple – related operations, properties, list, etc.
3. List – operations, related properties
4. Set – properties, associated operations, dictionary – operations, related properties.

13.1 Introduction to Dimensionality
13.2 Why Dimensionality Reduction
13.3 PCA
13.4 Factor Analysis
13.5 LDA

Hands-on Exercise –
Practice Dimensionality reduction Techniques : PCA, Factor Analysis, t-SNE, Random Forest, Forward and Backward feature

14.1 White Noise
14.2 AR model
14.3 MA model
14.4 ARMA model
14.5 ARIMA model
14.6 Stationarity
14.7 ACF & PACF

Hands-on Exercise –
1. Create AR model
2. Create MA model
3. Create ARMA model

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Python for Data Science Projects

What projects I will be working on this Python for Data Science course?

Project 01: Analysing the trends of COVID-19 with Python

Industry: Analytics

Problem Statement: Understanding, the trend of covid 19 spread and if the restrictions imposed by governments around the world has helped us curb the COVID cases and by what degree

Topics: In this project we will use Data Science and Python perfrom visualization to better understand the data we currently have on COVID 19 as well as using Time Series Analysis in order to make a perdiction about future cases if the current trend as observed thus far continues.

Highlights:

  • Using pandas to accumulate data from multiple data files
  • Using plotly (visualization library) to create interactive visualizations
  • Using facebooks prophet library to make timeseries models
  • Visualzing the perdiction by combining these technologies

Project 02: Analyzing the naming trends using Python

Problem Statement: The dataset is in Zipped format, we have to extract the dataset in the program, visualize the number of male and female babies born in a particular year, and find out popular baby names.

Topics: Algorithms, Python programming

Highlights:

  • Understanding the applications of data manipulation
  • Understanding how to extract only files that is having useful data
  • To understand the concepts of data visualization
  • To analyze baby names by sorting out top 100 birth counts

Project 03: Performing Analysis on Customer Churn Dataset

Problem Statement: Analysis of Employment reliability of employees in the telecom industry

Topics: Algorithms, Manipulation, Data Visualization, Python Language

Highlights:

  • Performing real time analysis of data by making use of multiple labels
  • Performing data visualization to understand the factor of reliability
  • Performing visual analysis of various columns to verify
  • Plotting charts to substantiate the findings in total

Project 04: Netflix-Recommendation system

Problem Statement: Analysis of movies dataset and recommendation of movies with respect to ratings.

Topics: Algorithms, Python, Recommendation engine

Highlights:

  • Understanding working with the combined data of movies and ratings dataset.
  • Performing data analysis on various labels in the data
  • Find the distribution of different ratings in the dataset
  • Train the SVD for the prediction of the model.

Project 05: Python Web Scraping for Data Science

In this project you will be introduced to the process of web scraping using Python. It involves installation of Beautiful Soup, web scraping libraries, working on common data and page format on the web, learning the important kinds of objects, Navigable String, deploying the searching tree, navigation options, parser, search tree, searching by CSS class, list, function and keyword argument.

Case Study 01: OOPS in Python

Problem Statement: Create multiple methods using OOPS concept

Topics: Parameterization, OOPS, Classes

Highlights:

  • A method ‘check_balance’ to check the remaining balance in the account
  • A method ‘withdraw’ to withdraw money from the bankFind the distribution of different ratings in the dataset
  • Over-ride the ‘withdraw’ method to check if minimum balance is maintained

Case Study 02: Working with NumPy

Problem Statement: Working with NumPy library to solve various problems in Python

Topics: NumPy

Highlights: 

  • Create 2D arrays
  • Initialize a numpy array of 5*5 dimensions
  • Perform simple arithmetic operations on these two numpy arrays

Case Study 03: Visualizing and Analyzing the Customer Churn dataset using R.

Problem Statement: Analyzing the data by building some aesthetic graphs to make better sense of the data.

Topics: Plots, ggplot2, Python Language

Highlights:

  • Understanding the working of ggplot2 package.
  • Understanding the applications of bar plots
  • Analyzing the data with the help of histogram graphs.
  • Observing some outliers in box-plots

Case Study 04: Building models with the help of Machine Learning Algorithms

Problem Statement: designing tree-based models on ‘Heart’ dataset.

Topics: ML Algorithms, Python Language

Highlights:

  • Performing real time data manipulation on the heart dataset.
  • Performing data visualization for multiple columnar data
  • Understanding and building tree-based model on top of the database
  • Designing a probabilistic classification model on the database.

Case Study 05: Visualizing and Analyzing the Customer Churn dataset using R.

Problem Statement: Analyzing the data by building some aesthetic graphs to make better sense of the data.

Topics: Plots, ggplot2, Python Language

Highlights

  • Understanding the working of ggplot2 package.
  • Understanding the applications of bar plots
  • Analyzing the data with the help of histogram graphs.
  • Observing some outliers in box-plots

Case Study 06: Building models with the help of Machine Learning Algorithms

Problem Statement: designing tree-based models on ‘Heart’ dataset.

Topics: ML Algorithms, Python Language

Highlights:

  • Performing real time data manipulation on the heart dataset.
  • Performing data visualization for multiple columnar data
  • Understanding and building tree-based model on top of the database
  • Designing a probabilistic classification model on the database.
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Python For Data Science Certification

This course is designed for clearing the Intellipaat Python for Data Science Certification Exam. The complete Python for Data Science course is created by industry experts for professionals to get the top jobs in the best organizations. The entire data science with python training includes real-world projects and case studies that are highly valuable.

The Intellipaat Certification is awarded upon successfully completing the project work and after reviewing by experts. The Intellipaat certification is recognized in some of the biggest companies like Cisco, Cognizant, Mu Sigma, TCS, Genpact, Hexaware, Sony, Ericsson among others.

Our Alumni works at top 3000+ companies

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Python Data Science Course Reviews

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Payal Raheja

Sr. Python Developer at Mindfire Solutions

I loved the way Intellipaat trainers taught the Python programming language as applicable to the Data Science domain. Great work!

Alexane Hofer

Software Engineer at Accenture

I liked the dedication of the Intellipaat support team when it came to resolving my queries regardless of the time of the day. Hats off to team Intellipaat!

Karunakara Rao P V

AGM- IT India(Infrastructure) at Siemens Gamesa

This instructor-led Python for Data Science training course is a beginner basic to advance in career. The trainer taught me all concepts from scratch. The study material really helped me to understand the subject properly. Hence, I believe this is the best data science with python course for beginners.

Sahana CP

Software Developer, Writer

I am happy that Intellipaat provides flexible scheduling of classes. They gave immense support during my classes. I am happy with the python for data science course material and instructor’s way of teaching as well. I definitely recommend this course to everyone.

Madhuri Immaneni

Sr. Technical Support Engineer

I got very good experience with the real-time projects provided by Intellipaat. The trainer was top class. The industry experience he carries is awesome. I am more than happy with the python for data science certification course. Looking forward to learn more Intellipaat online training courses.

Python Data Science Training FAQs

Why should I learn Python for Data Science from Intellipaat?

This Intellipaat Python for Data Science training will give you hands-on experience in mastering one of the best programming languages that is Python. In this online Python for Data Science course, you will learn about the basic and advanced concepts of Python including MapReduce in Python, Machine Learning, Hadoop streaming and also Python packages like Scikit and Scipy. You will be awarded the Intellipaat Course Completion Certificate after successfully completing the training course.

As part of this online Data Science with Python course, you will be working on real-time Python projects that have high relevance in the corporate world and step-by-step assignments, and the curriculum is designed by industry experts. Upon the completion of the Python for Data Science certification, you can apply for some of the best jobs in top MNCs around the world at top salaries. Intellipaat offers lifetime access to videos, course materials, 24/7 support and course material upgrading to the latest version at no extra fees. Hence, it is clearly a one-time investment.

At Intellipaat, you can enroll in either the instructor-led online training or self-paced training. Apart from this, Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.

Intellipaat is offering the 24/7 query resolution, and you can raise a ticket with the dedicated support team at anytime. You can avail of the email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our trainers.

You would be glad to know that you can contact Intellipaat support even after the completion of the training. We also do not put a limit on the number of tickets you can raise for query resolution and doubt clearance.

Intellipaat offers self-paced training to those who want to learn at their own pace. This training also gives you the benefits of query resolution through email, live sessions with trainers, round-the-clock support, and access to the learning modules on LMS for a lifetime. Also, you get the latest version of the course material at no added cost.

Intellipaat’s self-paced training is 75 percent lesser priced compared to the online instructor-led training. If you face any problems while learning, we can always arrange a virtual live class with the trainers as well.

Intellipaat is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.

You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.

Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.

You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.

Once you complete Intellipaat’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500companies.

Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.

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